Facilitating advanced Sentinel-2 analysis through a simplified computation of Nadir BRDF Adjusted Reflectance
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FOSS4G Europe 2024 Tartu46 / 156
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Mathematical analysisMultiplication signDifferent (Kate Ryan album)Mathematical analysisCubeLecture/Conference
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Transcript: English(auto-generated)
00:00
I will be talking about practically harmonizing Sentinel-2 data according to Nadir-BRDF adjustments. So this is very useful for people who is doing actually analysis that require this kind of satellite imagery, especially with different acquisition times.
00:23
This is really important. And also if you are, for example, working with data cubes, this will also help you a lot on it. It's based on Python, so if you are working on Python, this can be very helpful for you. So I will start saying what we are going to talk about. So first a little bit of introduction to Sentinel-2, BRDF, and the C-factor method
00:44
for getting Nadir-BRDF adjusted reflectance. Then we go to a little part of data cubes and then we end with the tool that we are going to present here, which is Sentinel-1 for getting the Nadir-BRDF adjusted reflectance or N-bar. For Sentinel-2 data cubes actually.
01:01
So just starting with Sentinel-2, well I guess most of us know what Sentinel-2 is, then we have a collection of two satellites out there with a multispectral instrument getting a very high resolution, actually a public high resolution satellite imagery in different
01:22
bands of electromagnetic spectrum. So for example, we have here the bands of Sentinel-2 and then we can see that for example we have 10 meter bands in RGB and also the near infrared, then we have 20 meter bands in the red edge, also a little bit of narrow near infrared and the shortwave infrared
01:44
bands, and then we have 16 meter bands for aerosols, water vapor, and this one for SIRUs, and this is just the Sentinel-2, so it's a very useful dataset.
02:00
And then we go to BRDF and for BRDF we get the definition of bi-directional reflectance distribution function and it is going to describe how actually reflectance changes according to the view and sun angles. So we usually kind of expect that the reflectance is just like the sun, get a sensor radiation
02:26
and then the sensor is going to receive this one, but this actually depends on a lot of other factors, so it's not the same and this changes according to where the satellite is, where the sun is, and a lot of other surface characteristics, so reflectance
02:44
is different according to where you look at it. And fortunately in 2000 for the MODIS actually sensor there was developed, well there was tested BRDF model, a kernel based BRDF model that is practically the sum of different
03:04
kernels or scattered mods, in this case the isotropic, the volumetric and the geometric kernel, they are going to actually describe different components of the BRDF, like this
03:21
is uniform scattering, this one is going to depend more on the kind of medium where the light is going to get scattered and this one is going more into how big geometric objects are going to interact with the light and the shadows are positioned and things like this. So these three are spectral parameters that can be derived for each one of the kernels
03:45
and these two are actually the kernels that depend on the solar senate angle, on the view senate angle, which is one of the sensors for the satellite and also the view solar relative azimuth angle, which is the difference between the azimuth of the view and the sun azimuth angles.
04:03
So this is the intro to BRDF and then the Z-factor method. The Z-factor method is just an algorithm, a very easy algorithm, that uses this BRDF model to adjust the surface reflectance to Nadir BRDF adjusted reflectance.
04:25
This started with these two papers by David Pierre Roy and he developed this method for reflectance using the previously shown MODIS BRDF model and it does the following. We have here the sensor, the sun and we have the corresponding angles.
04:45
So this is the view senate angle, this is the senate, this is the senate and this is the view senate angle, this is the sun senate angle, then we have the solar azimuth here, this is the north line, this is south line and we have here the view azimuth. Now the difference of these two is the relative azimuth and what this algorithm does is practically
05:05
take this sensor and put it in senate. So we have for all our acquisition times or our acquisition images always this adjusted Nadir view. So actually our images can be comparable across time and he does this in a very easy way.
05:26
So this is for example the M-bar for the specific band and as you can see this is just a scaling factor against the normal surface reflectance of that specific band and this is the C-factor
05:44
and here it's very cool because the C-factor is just the ratio of the BRDF model that we just presented before but between the numerator being set to zero in the senate angle. So in that way the BRDF model that we can compute here can be used to actually adjust
06:06
all of the images that we want to actually use for our analysis and that's how we can get Nadir BRDF adjusted reflectance. This was done for Landsat and then the spectral parameters were retrieved from the MODIS spectral
06:27
parameters so we have them here and then this was extended to Sentinel-2 and in Sentinel-2 we just use the same Landsat spectral parameters and additionally we add the ones for the
06:47
Landsat spectral parameters that were derived from MODIS-2. So here you can see for example how it changes when you use surface reflectance or Nadir BRDF reflectance data. This is surface, this is adjusted and this here is the difference.
07:05
You can see that this is the end of one of the swabs and this here is the difference of these two and then you can see that there are actually effects that we have to consider here or what is the reflectance value that we have there in these intersecting sections.
07:20
If we want to do analysis they can get messed up if we don't correct that, that's not negligible. So that's pretty important if we for example want to work with data cubes in as it was previously shown in the two talks ago. Data cubes are aligned with a
07:43
spatio-temporal grid. So our system data cubes are just multi-dimensional arrays of our system data that have their own dimensions, a set of grids, data and attributes and in the set of grids we have the space and we have the time. A very important part of the
08:03
system data cubes are our observation data cubes and this is even more relevant now because we can easily get data cubes like using for example specifications like a stack and if we have a data stored in the stack and then we can use different tools to get actually
08:23
the data from a stack and actually converting them as an observation data cubes in its array we can analyze large quantities of data in space and in time but before analyzing that it's very important actually to correct by these effects before. So that's why we
08:47
went and started the creation of this tool. The tool is called Send to Ember. This one is available in GitHub. If you want to install it you can just do pip install send to ember
09:02
or if you are using conda you can also go conda install from the conda force channel. It's also available there and it works in a very simple way. It works with set files like if you have your own sentinel level to April which is surface reflectance it actually gets all the
09:29
data that it needs for computing this factor and computing the nadirbya effectus reflectance using the metadata and the actual values of it. So it's going to use the granule metadata to actually do the c factor computation and then it's going to use the same metadata to extract
09:50
also the processing baseline. This is important so we can actually harmonize sentinel 2 first because if you remember after processing baseline number 4 all the reflectance values are shifted
10:05
so we have to shift this before doing the correction. Then the c factor is interpolated according to the spatial resolution of each one of the bands and then the ember is just calculated itself and saved in the same save file. But also we have here for data cubes
10:25
and in this case for data cubes that are computed or are extracted via stack. So the idea is that we have a data cube with multiple bands and multiple time steps and our specified bounding bots. We can use this data cube to go into the stack where we created it from and then we
10:48
get the metadata from it. We do the computation of the c factor for all of the time steps that were included there. We also extract the processing baseline so we can actually harmonize the data
11:00
first and then we do the interpolation of the c factor to create the ember computation but this is done for the entire data cube according to the resolution that you have and also according to the reference system that you have and also for the bands that you have. It doesn't have to be all of them if you don't need to and all of this is computed using x array and if you have it as a lazy array with Dask it can be computed in parallel too.
11:28
So from behind this is doing this. This is just getting the metadata and then from the metadata is just getting the same heat and azimuth view angles and also from the sun is getting the
11:41
same heat and azimuth angles. From this is then computing the kernels in this case just the geometric and the volumetric kernel because the isotopic one is uniform and then using the spectral parameters that were derived from David Roy papers we can compute the BRDF model. In this case this is the Rosli BRDF model that I showed at the beginning and then from this one
12:05
the c factor is just computed just in a very quick way using multi-dimensional arrays. Now it is just as simple as doing the multiplication between the c factor and the surface reflectance
12:21
for getting the Nadir BRDF. Adjust the reflectance for your whole data cube and well how to do it? Well for safe files it's very simple. You just import function to mbar dot mbar mbar underscore safe and then you just used this function and then you put here the path to your cell file and
12:42
it's going to do the whole computation. But if you want to do it for example with data cubes that you created from stack stack this is for example one from planetary computer and using stack stack you just do your whole pipeline as you used to. You get your endpoint collection the
13:00
bounds then you do the you open your catalog with the pi stack client and then you define the area where you want where you want to get the data cube from and then you do the search get all the items and then retrieve this stack of images using stack stack and then for converting it to Nadir BRDF adjust the reflectance you just have to use this mbar underscore stack
13:22
stack and then you put here and stack your data cube then you put the endpoint because we have to tell the function where to get the metadata from so it's gonna get it's gonna go to this exact same endpoint and get the metadata to compute the c factor and also what what is the name of the collection so it can go to it and easily compute everything and then it's going
13:44
to return another data array with the corrected values and this is another example using kubo kubo is just a high level wrapper for creating AI focused data cubes and by AI I mean
14:01
data cubes which the spatial dimensions the lengths of the spatial dimensions are the same like for example you usually in AI models use image patches or chips where you use 128 pixels by 128 pixels for example at different time dimensions at different time lengths
14:21
this is doing this kubo is just a package that creates data cubes out of my stack but just with a special dimensions of equal length and then for this one is just using this function frontend to mbar dot mbar you import mbar underscore kubo and then you put your cube there
14:40
and then it's going to return the same data cube but corrected and these are the three main functions and just to tell you how important it is let's check a little bit effects on reflectance in just one single cube this is a mbar composite of RGB for a single cube
15:05
in a forest site and then the rest of things that you are seeing here are the differences between surface reflectance and nadirbrdf adjusted reflectance and for example you can see that for example for RGB is maybe not that big the effect or the difference but when you start
15:25
getting close to near infrared for example red edge one red edge two red edge three and near infrared are pretty much affected by the by the by the brdf effects maybe not that much in in space in this case because this is a very small cube it's just like 200 by 200 pixels but if
15:45
you can see the temporal dimension which is this one here you can see that there are pretty big differences and if you want to do temporal analysis it's very important to consider this into to consider this in your analysis and also not just in reflectance itself if it's in reflectance
16:01
and then you get derived products from it like for example vegetation indices you will see also that there is an effect but it will depend also in the index like if for example you have here ndbi ndbi is almost not affected by it because it's normalizing everything but if you have other indices that are actually not that normalized you will start seeing the effects more
16:24
more greatly like for example this is near infrared reflectance of vegetation it's a little bit more affected when you use the kernel ndbi just start seeing more points even in space and in time they are getting affected and as i showed you before the red edge was pretty much affected by it this is the inverted red color filling that's that uses
16:46
that uses the all the three red edge bands in combination with the near infrared and if you check the the the differences between the index using mbar and using surface reflectance
17:00
well you see that the that the differences are actually pretty big here and if you are using a spectral indices maybe it's a good idea also to do the corrections before performing your analysis so uh that's for free for that's all now now for finishing and just i just wanted to
17:22
say the final remarks the rdf effects incentive to imagery are not negligible we should correct this uh the c-factor method can be used to convert a surface reflectance to nadir vrdf reflectance and this will minimize the vrdf effects and that's why we developed sent to ember which is an open source vital tool that uses the c-factor method for convert
17:43
for converting the surface reflectance of sentinel 2 to nadir vrdf adjusted reflectance and finally sent to ember works with single safe files but it also works for data cubes that were created from cloud store a stack data catalogs and with that i just want to say thank you
18:13
also been fantastically in time make use of the time uh do we have questions we have lots of time for questions
18:34
thank you for this interesting presentation um i was looking in this methodology of adjustments
18:41
and it seems that it really needs to be done what is your take should it be done centrally like for example uh on compared to store they should distribute already adjusted imagery as we know that they're they're producing products which are already like more user friendly and this seems like a thing that shouldn't we be doing on each computer on
19:02
our own because it could be done just once and published like ready data and so on sorry can you repeat at the beginning of the question it was pretty hard to hear you from here sorry yeah do you think that this adjustment should be done centrally like by isa on uh and
19:20
pre-distributed for the copernicus data store instead of like that we just download files and run it again and again again and so on i mean that would be great yes but that depends also in the user i mean this is just one algorithm right and there might be others
19:41
there and people developing other things right so i think at the end this is more a choice of the user on what to do and this is one tool that goes for it in a simple way for data keeps for example and also for the files that they deliver in the level 2a product but it will depend i think more on the user side more questions um maybe to extend
20:20
like the previous question um can you i'm definitely not an expert on brdf but since you are can you extend a bit on like the state of brdf in the community like what are the biggest remaining challenges why why is there no like official product um published for example
20:40
right okay i mean actually the only official product that i know is the modis one they they have in bar and it's using the brdf model that i presented at the beginning it's a actually a pretty simple model it just has like three components and they actually do not consider for example different land core types and how are they influencing the brdf effects
21:06
and many other things so it's actually not that simple to have a brdf model and this one is a very simplified one of what we can get um so that's also why i say maybe it's it's not
21:22
on part of for example esa saying okay we deliver this one and that's it um but it's probably a whole branch of research and just also in the specific uh sensor i mean thank you
21:44
we still have uh one or two one three more minutes hello i would like to ask is then lansat 8 and sentinel 2 data similar because they have the difference if they use the
22:05
sorry i can just repeat the question again and is the sentinel 2 data and lansat 8 data similar if you use this processor processing okay you're asking if lansat and sentinel 2 are using like the same
22:25
no if they are similar if they are similar enough to use okay okay thank you well i wouldn't say they are exactly similar they sure probably similar bands but they are
22:41
also not they also don't have the same spectral response function for each one of the bands so i thought this algorithm was developed for lansat it can also be used for sentinel 2 it's also depending on the fact that it's also dependent on their reflectance values itself
23:01
and its calendar reflectance values and in that case it kind of serves to harmonize both using in this case for example the same model yeah but all of them are actually based on the spectral parameters of the brdf model of modis it's just a combination of a lot of stuff
23:21
yeah that is that is a tricky situation still although the there's some projects going on that try to align the latest the later lansat and and sentinel ones but the frequencies the spectra are not comparable directly so the numbers are usually it's not you cannot just
23:41
use one thing and play with the other day you have to be always a bit careful there yeah but it's getting it's getting better i mean there's all this research in the data fusion but that would be again a different mesh yes machine mix and then you would have to recalibrate it again differently okay um um how much of your day do you code
24:05
a day i just want to need it no of your normal work day so how much time of your normal work day do you spend coding oh things like this uh well coding most of the time like 90 percent of the time in court okay i think uh when if you have another question oh yeah
24:27
sorry i didn't see that um i want to go back with the previous question a bit i didn't completely understand uh like sentinel to um has two different platforms so can i use this to
24:40
really compare then the the results for this two platforms a and b do you want to compare yeah yeah exactly so you have it now had uh the differences with lancet and sentinel because there are different platforms so you have to be careful in in the comparisons with this method but now you have that for sentinel with two different platforms i didn't quite
25:05
understand how that then works i'm not pretty sure i understood the question i think um then maybe for clarification then we can try to find each other in the in the break